 Chapter 7.7.1: Describing colleges. Popular magazines rank colleges and universiti...
 Chapter 7.7.2: Affording college. From time to time, the Department of Education e...
 Chapter 7.7.3: Changes in how we watch. Movies earn income from many sources other...
 Chapter 7.7.4: What we watch now. The previous exercise looked at movie studio inc...
 Chapter 7.7.5: Growing icicles. Table 4.2 (page 98) gives data on the growth of ic...
 Chapter 7.7.6: Weights arent Normal. The heights of people of the same sex and sim...
 Chapter 7.7.7: Returns on stocks arent Normal. The 99.7 part of the 689599.7 rule ...
 Chapter 7.7.8: Remember what you ate. How well do people remember their past diet?...
 Chapter 7.7.9: Cicadas as fertilizer? Every 17 years, swarms of cicadas emerge fro...
 Chapter 7.7.10: Hot mutual funds? Investment advertisements always warn that pastpe...
 Chapter 7.7.11: More about cicadas. Lets examine the distribution of seed mass for ...
 Chapter 7.7.12: More on hot funds. Continue your study of the returns for Fidelity ...
 Chapter 7.7.13: Outliers? In Exercise 7.11, you noticed that the smallest and large...
 Chapter 7.7.14: Where does the water go? Here are data on the amounts of water with...
 Chapter 7.7.15: Bestselling soft drinks. Here are data on the market share of the ...
 Chapter 7.7.16: Presidential elections. Here are the percents of the popular vote w...
 Chapter 7.7.17: The Mississippi River. Table 7.1 gives the volume of water discharg...
 Chapter 7.7.18: More on the Mississippi River. The data in Table 7.1 are a time ser...
 Chapter 7.7.19: A big toe problem. Hallux abducto valgus (call it HAV) is a deforma...
 Chapter 7.7.20: More on a big toe problem. The HAV angle data in the previous exerc...
 Chapter 7.7.21: Predicting foot problems. Metatarsus adductus (call it MA) is a tur...
 Chapter 7.7.22: Predicting foot problems, continued.(a) Find the equation of the le...
 Chapter 7.7.23: Data on mice. For a biology project, you measure the tail length (c...
 Chapter 7.7.24: Catalog shopping (optional). What is the most important reason that...
 Chapter 7.7.25: 7.25 How are schools doing? (optional) The nonprofit group Public A...
 Chapter 7.7.26: Weighing bean seeds. Biological measurements on the same species of...
 Chapter 7.7.27: Breaking bolts. Mechanical measurements on supposedly identical obj...
 Chapter 7.7.28: Scatterplot. Plot the weight of the bar of soap against day. Is the...
 Chapter 7.7.29: Regression. Find the equation of the leastsquares regression line ...
 Chapter 7.7.30: Prediction? Use the regression equation in the previous exercise to...
 Chapter 7.7.31: Statistics for investing. Joes retirement plan invests in stocks th...
 Chapter 7.7.32: Initial public offerings. The business magazine Forbes reports that...
 Chapter 7.7.33: Moving in step? One reason to invest abroad is that markets in diff...
 Chapter 7.7.34: Interpreting correlation. The same article that claims that the cor...
 Chapter 7.7.35: Coaching for the SATs. A study finds that high school students who ...
 Chapter 7.7.36: Change in the Serengeti. Longterm records from the Serengeti Natio...
 Chapter 7.7.37: Prey attract predators. Here is one way in which nature regulates t...
 Chapter 7.7.38: Extrapolation. Your work in Exercise 7.36 no doubt included a regre...
 Chapter 7.7.39: When does the ice break up? We have 89 years of data on the date of...
 Chapter 7.7.40: Global warming? Because of the high stakes, the falling of the trip...
 Chapter 7.7.41: More on global warming. Sidebyside boxplots offer a different loo...
 Chapter 7.7.42: Save the eagles. The pesticide DDT was especially threatening to ba...
 Chapter 7.7.43: Thin monkeys, fat monkeys. Animals and people that take in more ene...
 Chapter 7.7.44: 7.45 Weeds among the corn. Lambsquarter is a common weed that inte...
 Chapter 7.7.45: Weeds among the corn, continued. We can also use regression to anal...
 Chapter 7.7.46: Is Old Faithful Faithful? Write a response to Questions 1 and 3 for...
 Chapter 7.7.47: Checkmating and Reading Skills. Write a report based on Question 1 ...
 Chapter 7.7.48: Counting Calories. Respond to Questions 1, 4, and 6 for this case s...
 Chapter 7.7.49: Mercury in Floridas Bass. Respond to Question 5. (Scatterplots, for...
 Chapter 7.7.50: Brain Size and Intelligence. Write a response to Question 3. (Scatt...
 Chapter 7.7.51: Acorn Size and Oak Tree Range. Write a report based on Questions 1 ...
 Chapter 7.7.52: Surviving the Titanic. Answer Questions 1, 2, and 3. (Twoway tables.)
Solutions for Chapter Chapter 7: Exploring Data: Part I Review
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 7: Exploring Data: Part I Review
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Since 52 problems in chapter Chapter 7: Exploring Data: Part I Review have been answered, more than 7733 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter Chapter 7: Exploring Data: Part I Review includes 52 full stepbystep solutions.

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

Causeandeffect diagram
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

Conidence coeficient
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

Correlation
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

Design matrix
A matrix that provides the tests that are to be conducted in an experiment.

Dispersion
The amount of variability exhibited by data

Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

Error variance
The variance of an error term or component in a model.

Factorial experiment
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

False alarm
A signal from a control chart when no assignable causes are present

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .

Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .